arXiv subjects

Mattia Fabiani

Publications and source records attributed to Mattia Fabiani.

3 recordsLinked to original sources

arXiv2026

Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces

The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred data. This paper presents an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) system that operates as an Extreme Learning Machine (ELM) to execute Over-The-Air (OTA) binary classification. To reduce hardware complexity, the receiver is equipped with cascaded metasurfaces terminating in a single radio-frequency chain. A front metasurface layer applies a fixed nonlinear response to the incoming signal, acting as the ELM's activation function. Subsequent tunable linear metasurface layers physically approximate the trained network weights directly in the wave domain. Numerical evaluations across diverse datasets showcase that our XL MIMO architecture achieves classification accuracy comparable to idealized digital models, thereby proving the viability of low-complexity, wave-domain OTA learning.

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arXiv2026

Over-The-Air Extreme Learning Machines with XL Reception via Nonlinear Cascaded Metasurfaces

The recently envisioned goal-oriented communications paradigm calls for the application of inference on wirelessly transferred data via Machine Learning (ML) tools. An emerging research direction deals with the realization of inference ML models directly in the physical layer of Multiple-Input Multiple-Output (MIMO) systems, which, however, entails certain significant challenges. In this paper, leveraging the technology of programmable MetaSurfaces (MSs), we present an eXtremely Large (XL) MIMO system that acts as an Extreme Learning Machine (ELM) performing binary classification tasks completely Over-The-Air (OTA), which can be trained in closed form. The proposed system comprises a receiver architecture consisting of densely parallel placed diffractive layers of XL MSs, also known as Stacked Intelligent Metasurfaces (SIM), followed by a single reception radio-frequency chain. The front layer facing the XL MIMO channel consists of identical unit cells of a fixed NonLinear (NL) response, whereas the remaining layers of elements of tunable linear responses are utilized to approximate OTA the trained ELM weights. Our numerical investigations showcase that, in the XL regime of MS elements, the proposed XL-MIMO-ELM system achieves performance comparable to that of digital and idealized ML models across diverse datasets and wireless scenarios, thereby demonstrating the feasibility of embedding OTA learning capabilities into future wireless systems.

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arXiv2025

Nonlinear EM-based Signal Processing

The use of high-frequency bands, combined with antenna arrays containing an extremely large number of elements (XL-MIMO), is pushing current technology to its limits in terms of hardware complexity, latency, and power consumption. A promising approach to achieving scalable and sustainable solutions is to shift part of the signal processing directly into the electromagnetic (EM) domain. In this paper, we investigate novel architectures that harness the interaction of reconfigurable passive linear and nonlinear (NL) scattering elements positioned in the reactive near field of signal sources. The objective is to enable multifunctional linear and NL EM signal processing to occur directly "over-the-air." Numerical results highlight the potential to significantly reduce both system complexity and the number of RF chains, while still achieving key performance metrics in applications such as direction-of-arrival and position estimation, without the need for additional analog or digital processing.

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